NDVI is the most widely used vegetation index in remote sensing, and calculating it in QGIS takes about five minutes once you know the steps.

NDVI exploits a simple fact about plants: healthy leaves absorb red light for photosynthesis and reflect near-infrared light strongly. The index combines those two bands into a number between -1 and 1, where values near 1 mean dense, healthy vegetation.

Which bands you need depends on your imagery. For Sentinel-2, use band 8 (near-infrared) and band 4 (red). For Landsat 8 or 9, use band 5 and band 4. In QGIS, open the Raster Calculator and type the formula: near-infrared minus red, divided by near-infrared plus red.

Style the result with a red-to-green pseudocolor ramp so the map tells a story: irrigated fields glow green, stressed patches show yellow, and bare ground stays red or brown.

Read NDVI honestly, because it has limits. It saturates in very dense vegetation, is sensitive to soil background and shadows, and clouds produce nonsense values that must be masked out.

Put NDVI to work with a repeatable workflow: calculate it for your area, clip it to your fields, and compare with an earlier date. The change map is usually more useful than any single NDVI image.

Once NDVI is routine, explore its relatives, because no single index answers every question. The Enhanced Vegetation Index, or EVI, corrects for some of NDVI's weaknesses in dense canopies and hazy atmospheres. The Normalized Difference Water Index, or NDWI, highlights open water and plant water content using different band combinations. The Normalized Difference Built-up Index helps separate urban areas from bare soil. All of these are calculated the same way in QGIS — a raster calculator expression over two bands — so learning one index teaches you the whole family. The skill is not memorizing formulas, but knowing which index matches your question, and checking its result against what you know is on the ground before you trust it.